class_docstr: The autoencoder-based multivariate time series anomaly detector.
  This detector utilizes an autoencoder to infer the correlations between
  different time series and estimate the joint distribution of the variables
  for anomaly detection.
  
  - paper: `Pierre Baldi. Autoencoders, Unsupervised Learning, and Deep Architectures. 2012.
    <https://proceedings.mlr.press/v27/baldi12a.html>`_
code: def _build_model(self, dim):
      model = AEModule(
          input_size=dim * self.k,
          hidden_size=self.hidden_size,
          layer_sizes=self.layer_sizes,
      )
      return model
  
imports: from typing import Sequence
  import numpy
  import pandas
  from merlion.models.base import NormalizingConfig
  from merlion.models.anomaly.base import DetectorBase, DetectorConfig
  from merlion.post_process.threshold import AggregateAlarms
  from merlion.utils.misc import ProgressBar, initializer
  from merlion.models.utils.rolling_window_dataset import RollingWindowDataset
label_desc: Use image node_id 4 for calling the AutoEncoder obj's underlying member method code with example usage: obj._build_model(dim) and returns: model